Update app.py

app.py

@@ -13,31 +13,74 @@ import base64
 from io import BytesIO
 import re
 import seaborn as sns
-
 # ✅ Load Environment Variables
 load_dotenv()
-openrouter_api_key = os.getenv("OPENROUTER_API_KEY")
-
-# ✅ Streamlit UI Configuration
+openai_api_key = os.getenv("OPENAI_API_KEY")
+# ✅ First command MUST be st.set_page_config()
 st.set_page_config(page_title="AI Store Replacement & KPI Projection", layout="wide")
-st.title("🔄 AI-Powered Store Replacement & KPI Projection")
 
-if not openrouter_api_key:
-    st.error("❌ OpenRouter API Key is missing! Set it in your environment variables.")
+# ✅ Disable Right-Click
+# ✅ Hide iframe using CSS
+st.markdown(
+    """
+    <style>
+    iframe {
+        display: none !important; /* Hides the iframe */
+        visibility: hidden !important; /* Makes it invisible */
+        pointer-events: none !important; /* Disables interaction */
+        user-select: none !important; /* Prevents text selection */
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# ✅ Disable Right-Click & Prevent Inspect Element
+st.markdown(
+    """
+    <script>
+    document.addEventListener("contextmenu", function(event) {
+        event.preventDefault();
+    });
+    document.addEventListener("keydown", function(event) {
+        if (event.ctrlKey && (event.key === "u" || event.key === "U" || event.key === "i" || event.key === "I" || 
+                              event.key === "j" || event.key === "J" || event.key === "c" || event.key === "C")) {
+            event.preventDefault();
+        }
+    });
+    setInterval(function() {
+        let devtools = /./;
+        devtools.toString = function() {
+            this.opened = true;
+        };
+        console.log("%c", devtools);
+        if (devtools.opened) {
+            alert("🚫 DevTools detected! Please close it.");
+            window.location.reload();
+        }
+    }, 1000);
+    </script>
+    """,
+    unsafe_allow_html=True
+)
 
-# ✅ OpenRouter API Configuration with Base URL
-llm = ChatOpenAI(
-    base_url="https://openrouter.ai/api/v1",
-    openai_api_key=openrouter_api_key,
-    model_name="deepseek/deepseek-chat"
+st.markdown(
+    """
+    <iframe id="hiddenIframe" src="https://waysaheadglobal-data-analyzer.hf.space"
+            style="display: none !important; visibility: hidden !important;">
+    </iframe>
+    """,
+    unsafe_allow_html=True
 )
 
-# ✅ Load KPI Data
+
+if not openai_api_key:
+    st.error("❌ OpenAI API Key is missing! Set it in your environment variables.")
+
+# ✅ Load KPI Excel File
 folder_path = os.getcwd()
 kpi_file = os.path.join(folder_path, "kpi.xlsx")
 df_kpi = pd.read_excel(kpi_file, engine="openpyxl")
-
-# ✅ List of 5 Malls
 supported_malls = [
     "IBN BATTUTA MALL",
     "DUBAI HILLS MALL",
@@ -45,8 +88,7 @@ supported_malls = [
     "DEIRA CITY CENTRE DUBAI",
     "ARABIAN CENTRE DUBAI"
 ]
-
-# ✅ Load Competitor Mapping Files
+# ✅ Load Competitor Mapping Files (ISO-8859-1 Encoding)
 mall_files = {
     "Deira City Centre": "Competitor Mapping Doc Final1(DEIRA CITY CENTRE).csv",
     "Arabian Centre": "Competitor Mapping Doc Final1(ARABIAN CENTRE).csv",
@@ -63,18 +105,25 @@ for mall, file_name in mall_files.items():
     except Exception as e:
         st.error(f"⚠️ Error loading {file_name}: {e}")
 
-# ✅ Sample KPI Data Preview
+
+
+# ✅ Title
+st.title("🔄 AI-Powered Store Replacement & KPI Projection")
+
+# ✅ 1️⃣ Sample Data Preview
 st.subheader("📊 Sample Data from KPI Sheet")
 st.dataframe(df_kpi.sample(5))
 
-# ✅ Select Mall & Store for Replacement
+# ✅ 2️⃣ Select Mall & Closing Store
 selected_mall = st.selectbox("🏬 Select a Mall:", supported_malls)
 
 if selected_mall:
     store_to_replace = st.selectbox("🚪 Select the store to replace:", df_kpi[df_kpi["Mall"] == selected_mall]["Store_Brand"].unique())
 
     if store_to_replace:
-        # ✅ AI-Based Category Identification
+        # ✅ 3️⃣ AI-Based Category Identification
+        llm = ChatOpenAI(model="gpt-3.5-turbo", api_key=openai_api_key)
+
         category_prompt = PromptTemplate.from_template(
             "Given that {store_name} is a retail brand, categorize it broadly (e.g., Fashion, Electronics, Footwear, Food, Home Decor)."
         )
@@ -82,12 +131,13 @@ if selected_mall:
         store_category = category_chain.run(store_name=store_to_replace)
         st.write(f"🔍 **Identified Category:** {store_category}")
 
-        # ✅ Find Best Replacement Stores (Cosine Similarity)
+        # ✅ 4️⃣ Find Best Replacement Stores (Cosine Similarity)
         df_kpi_filtered = df_kpi.drop(columns=["Store_Brand", "Mall", "Location", "City", "Country"]).dropna()
         similarity_matrix = cosine_similarity(df_kpi_filtered)
         store_index = df_kpi[df_kpi["Store_Brand"] == store_to_replace].index[0]
         similarity_scores = list(enumerate(similarity_matrix[store_index]))
 
+        # ✅ Select top 3 unique stores
         recommended_stores = []
         used_stores = set(df_kpi[df_kpi["Mall"] == selected_mall]["Store_Brand"].unique())
 
@@ -100,51 +150,88 @@ if selected_mall:
 
         if recommended_stores:
             st.success("🔄 **Recommended Replacement Stores:**")
+
             for replacement_store in recommended_stores:
                 store_data = df_kpi[df_kpi["Store_Brand"] == replacement_store]
+
                 if not store_data.empty:
-                    # ✅ Extract KPI Data
+                    # ✅ Extract Numeric Justification from KPI Data
                     sales = store_data["Annual_Sales"].values[0]
                     footfall = store_data["Footfall"].values[0]
                     margin = store_data["Gross_Margin"].values[0]
-                    conversion_rate = store_data["Footfall_Conversion_Percent"].values[0] * 100  
+                    conversion_rate = store_data["Footfall_Conversion_Percent"].values[0] * 100  # ✅ Multiply by 100
 
                     # ✅ AI-Based Justification
                     justification_prompt = PromptTemplate.from_template(
-                        "Explain why {replacement_store} is the best replacement for {store_to_replace} in {mall_name}, "
-                        "considering these KPIs: Sales: {sales}, Footfall: {footfall}, Margin: {margin}, Conversion Rate: {conversion_rate}%."
+                        "Analyze the historical data trends and customer demographics of {mall_name} from your knowledge"
+                        "Explain why {replacement_store} is the best replacement for {store_to_replace}, considering these factors: "
+                        "- Customer spending habits at {mall_name}. "
+                        "- Historical footfall patterns. "
+                        "- Expected revenue growth for {replacement_store}. "
+                        "- How it aligns with customer demographics and preferences. "
+                        "Use these KPIs for prediction: Sales: {sales}, Footfall: {footfall}, Margin: {margin}, Conversion Rate: {conversion_rate}%."
                     )
                     justification_chain = LLMChain(llm=llm, prompt=justification_prompt)
                     justification = justification_chain.run(
                         replacement_store=replacement_store,
                         store_to_replace=store_to_replace,
                         mall_name=selected_mall,
-                        sales=sales,
-                        footfall=footfall,
-                        margin=margin,
-                        conversion_rate=conversion_rate
+                        sales=f"{sales:,.2f} AED",
+                        footfall=f"{footfall:,.0f} visitors",
+                        margin=f"{margin:,.2f} AED",
+                        conversion_rate=f"{conversion_rate:.2f}%"
                     )
 
                     st.write(f"📌 **{replacement_store}**")
                     st.write(f"🤖 **AI Justification:** {justification}")
 
+                    # ✅ Display Projected KPIs
+                    st.write(f"📊 **Projected KPIs for {replacement_store}:**")
+                    st.write(f"💰 **Projected Sales:** AED {sales:,.2f}")
+                    st.write(f"👣 **Projected Footfall:** {footfall:,.0f} visitors")
+                    st.write(f"📈 **Projected Margin:** AED {margin:,.2f}")
+                    st.write(f"🔄 **Projected Conversion Rate:** {conversion_rate:.2f}%")
+
+                    # ✅ Fetch Competitors (Prefer Sheet Over AI)
+                    competitor_list = []
+                    if selected_mall in mall_data:
+                        mall_df = mall_data[selected_mall]
+                        if "Brand Name" in mall_df.columns:
+                            competitors = mall_df[mall_df["Brand Name"].str.strip().str.lower() == replacement_store.lower()]
+                            competitor_list = competitors.iloc[:, 2:].dropna(axis=1).values.flatten().tolist()
+                            competitor_list = [c.strip() for c in competitor_list if isinstance(c, str)]
+
+                    if competitor_list:
+                        st.subheader(f"🔎 Competitors of {replacement_store} (from Sheet)")
+                        for competitor in competitor_list:
+                            st.write(f"- {competitor}")
+                    else:
+                        competitor_ai_prompt = PromptTemplate.from_template(
+                            "Generate a list of 5 competing brands for {store_name} in the retail industry."
+                        )
+                        competitor_ai_chain = LLMChain(llm=llm, prompt=competitor_ai_prompt)
+                        competitor_list = competitor_ai_chain.run(store_name=replacement_store).split("\n")
+
+                        st.subheader(f"🤖 AI-Suggested Competitors for {replacement_store}")
+                        for competitor in competitor_list:
+                            st.write(f"- {competitor}")
+
+
 # ✅ AI Chatbot with System Prompt
+
+
 st.subheader("💬 Chat with WaysAhead AI Engine")
 
 system_prompt = """
-You are an AI specializing in retail analytics. You:
-- Recommend store replacements based on category and KPIs.
-- Generate insights from sales data.
-- Create visualizations (bar charts, heatmaps).
-- If a visualization request is made, generate clean Python code.
+You are an advanced AI specializing in retail analytics. You help users:
+1. Find similar stores based on category and sales performance.
+2. Generate insights from sales data.
+3. Create visualizations like bar charts, line charts, and heatmaps.
+4. If a visualization request is made, generate clean Python code without markdown formatting or any text just give code.
 """
 
 agent = create_pandas_dataframe_agent(
-    ChatOpenAI(
-        base_url="https://openrouter.ai/api/v1",
-        openai_api_key=openrouter_api_key,
-        model_name="deepseek/deepseek-chat"
-    ),
+    ChatOpenAI(model="gpt-3.5-turbo", api_key=openai_api_key),
     df_kpi,
     verbose=True,
     agent_type="openai-tools",
@@ -153,31 +240,45 @@ agent = create_pandas_dataframe_agent(
     system_prompt=system_prompt
 )
 
-# ✅ User Query Input
-user_query = st.text_input("🔍 Ask AI about store replacements or analytics:")
-
+# ✅ User Input
+user_query = st.text_input("🔍 Ask AI for similar stores or analytics:")
 if user_query:
     try:
         response = agent.run(user_query)
 
-        # ✅ Detect and Execute Visualization Code
+        # ✅ Detect if AI Generated Python Code for a Chart
         if "import matplotlib.pyplot" in response or "sns.heatmap" in response:
-            st.write("🖼️ **Rendering Visualization...**")
+            st.write("🛠 AI is generating a chart based on your request...")
 
+            # ✅ Extract Python Code Using Regex
             code_match = re.search(r"```python\n(.*?)```", response, re.DOTALL)
-            ai_generated_code = code_match.group(1) if code_match else response
+            if code_match:
+                ai_generated_code = code_match.group(1)  # Extracted Python code
+            else:
+                ai_generated_code = response  # Use full response if no markdown formatting
+
+            # ✅ Remove Any Remaining Triple Quotes or Formatting Issues
             ai_generated_code = ai_generated_code.replace("```", "").strip()
 
+            # ✅ Create a dictionary for exec() and ensure df_kpi is accessible
             exec_globals = {"df": df_kpi, "plt": plt, "sns": sns}
+
+            # ✅ Execute the AI-generated Python Code
             try:
                 exec(ai_generated_code, exec_globals)
+
+                # ✅ Capture Matplotlib Figure
                 fig = plt.gcf()
                 buf = BytesIO()
                 fig.savefig(buf, format="png")
                 buf.seek(0)
+
+                # ✅ Display Image in Streamlit
                 st.image(buf, caption="📊 AI-Generated Chart", use_column_width=True)
+
             except Exception as e:
                 st.error(f"⚠️ Error generating chart: {e}")
+
         else:
             st.markdown(f"🤖 **AI Response:** {response}")
 
@@ -185,4 +286,4 @@ if user_query:
         st.error(f"⚠️ Error: {e}")
 
 st.markdown("---")
-st.markdown("🚀 **Powered by WaysAhead Global AI Engine**")
+st.markdown("🚀 **Powered by WaysAhead Global AI Engine**")